GE
Energy Consulting
Pan-Canadian Wind
Integration Study (PCWIS)
Section 4: Assumptions and Scenarios
Prepared for:
Canadian Wind Energy Association (CanWEA)
Prepared by:
GE Energy Consulting
October 14, 2016 (Revision 3)
Pan-Canadian Wind Integration Study (PCWIS)
Legal Notices
Legal Notices
This report section was prepared by General Electric International, Inc. (GEII), acting through
its Energy Consulting group (GE), as an account of work sponsored by Canadian Wind Energy
Association (CanWEA). Neither CanWEA nor GE, nor any person acting on behalf of either:
1. Makes any warranty or representation, expressed or implied, with respect to the use
of any information contained in this report, or that the use of any information,
apparatus, method, or process disclosed in the report may not infringe privately
owned rights.
2. Assumes any liabilities with respect to the use of or for damage resulting from the
use of any information, apparatus, method, or process disclosed in this report.
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Final Report – Section 4
Pan-Canadian Wind Integration Study (PCWIS)
Acknowledgements
Acknowledgements
The Pan-Canadian Wind Integration Study (PCWIS) was co-funded by Natural Resources
Canada (NRCan) through the ecoEnergy Innovation Initiative (ecoEII) and the Canadian Wind
Energy Association (CanWEA), with in kind support from each organization.
While produced with financial support from Natural Resources Canada, its contents do not
necessarily reflect the opinions of the Government of Canada.
The Pan-Canadian Wind Integration Study could not have been undertaken without the
generously offered time, commitment and data from members of the Technical Advisory
Committee (TAC), and the support and feedback provided by CanWEA, NRCan, and DNV GL,
the project advisor to CanWEA.
CanWEA is grateful for the support and guidance offered by the TAC, and wishes to thank
the members and the organizations they represent for the important contributions they
have made to this study. It should be noted that while members of the TAC were
instrumental in ensuring the successful delivery of this work, the findings, opinions,
conclusions and recommendations presented herein do not necessarily reflect those of the
TAC members or the organizations they represent.
Technical Advisory Committee Members:

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








Alberta Electric System Operator (AESO)
BC Hydro
Hydro Quebec
Independent Electricity System Operator (IESO)
ISO-New England (ISO-NE)
Manitoba Hydro
Midcontinent Independent System Operator (MISO)
National Renewable Energy Laboratory (NREL)
New York Independent System Operator (NYISO)
SaskPower
Utility Variable-Generation Integration Group (UVIG)
Western Electricity Coordinating Council (WECC)
The project team and CanWEA also acknowledge and thank Environment and Climate
Change Canada which performed the mesoscale atmospheric modeling and provided raw
wind-related data for the wind profiling and forecasting.
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Pan-Canadian Wind Integration Study (PCWIS)
Contact Information
Contact Information
The Pan-Canadian Wind Integration Study Report was prepared by General Electric
International, Inc. (GEII); acting through its Energy Consulting group (GE) – part of GE Energy
Connections - based in Schenectady, NY, and submitted to CanWEA. Technical and
commercial questions and any correspondence concerning this document and the study
should be referred to:
The Canadian Wind Energy Association
Tracy Walden
Director – Media and Communications
1600 Carling Avenue, Suite 710
Ottawa, Ontario, Canada K1Z 1G3
+1 (800) 922-6932 Ext. 252
[email protected]
GE Project Manager
Bahman Daryanian
Technical Director
GE Energy Connections, Energy Consulting
8 Sable Court West
East Amherst, NY, USA 14051-2210
+1 (716) 479-9629
[email protected]
GE Energy Consulting
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PCWIS Final Report Table of Content
PCWIS Final Report Table of Content
1. Report Summary
2. Introduction and Scope
3. Wind Data Development
4. Assumptions and Scenarios
5. Statistical and Reserve Analysis
6. Scenario Analysis
7. Transmission Reinforcements
8. Sensitivity Analysis
9. Sub-Hourly Analysis
10. Wind Capacity Valuation
11. Appendices and References
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Table of Contents
Table of Contents
4 Assumptions and Scenarios
4.1
14
Study Assumptions
14
4.1.1
Model Footprint
14
4.1.2
Canadian Power System Overview
16
4.1.3
General Modeling Assumptions
18
4.1.4
Thermal Generator Modeling
19
4.1.5
Hydro Generator Modeling
20
4.1.6
Wind Generator Modeling
24
4.1.7
Curtailment
25
4.1.8
Fuel Price Projections
26
4.1.9
Load Projections
28
4.1.10 Transmission
30
4.1.11 Generation Expansion Methodology
35
4.2 Study Scenarios
4.3
38
4.2.1
Selected Scenarios
38
4.2.2
Wind Additions in the United States
44
Wind Site Selections
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List of Figures
List of Figures
Figure 4-1: Model Topology of the Eastern and Western Interconnections .............................................................................. 15
Figure 4-2: Installed Capacity by Type, by Province (2025, without wind additions) ............................................................. 17
Figure 4-3: Monthly and Annual Hydro Capacity Factor Variation, Alberta Example ........................................................... 22
Figure 4-4: Net Load Hydro Scheduling Methodology Example ....................................................................................................... 23
Figure 4-5: 2025 Natural Gas Price Assumptions by Pricing Node (2016 C$/GJ) .................................................................... 27
Figure 4-6: 2025 Monthly Load Energy and Peak Demand for Canada ....................................................................................... 30
Figure 4-7: High Voltage Transmission Network Map of Canada.................................................................................................... 30
Figure 4-8: IESO Intra-Provincial Transmission Interfaces .................................................................................................................. 34
Figure 4-9: Locations of Selected Wind Plants by Study Scenario .................................................................................................. 41
Figure 4-10: Study Scenario Overview ........................................................................................................................................................... 42
Figure 4-11: Installed Wind Capacity by Scenario, by Province........................................................................................................ 44
Figure 4-12: Average Available Capacity Factor by Scenario, by Province ................................................................................ 44
Figure 4-13: Wind Grid Cell Locations............................................................................................................................................................. 46
Figure 4-14: Red Dots Represent Wind Plants and Black Dots Represent Grid Cells ............................................................ 47
Figure 4-15: Example of 10 km x 10 km Areas That Are Tiled To Identify Grid Cells To Be Aggregated Into Wind
Plants ................................................................................................................................................................................................................... 48
Figure 4-16: Number of Wind Sites at Different Rated Capacities .................................................................................................. 48
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List of Tables
List of Tables
Table 4-1: List of Provincial Grid Operators and Market Structures ............................................................................................... 16
Table 4-2: Installed Capacity by Type (MW), by Province (2025, without wind additions) ................................................... 17
Table 4-3: 2025 Natural Gas Price Assumptions by Pricing Node (2016 C$/GJ) ...................................................................... 27
Table 4-4: 2025 Coal, Oil, Uranium and Other Fuel Price Assumptions (2016 C$/GJ) ........................................................... 28
Table 4-5: 2025 Load Forecast by Province ................................................................................................................................................ 29
Table 4-6: Inter-Provincial Transmission Interface Limits .................................................................................................................... 32
Table 4-7: International Transmission Interface Limits between Canada and USA .............................................................. 33
Table 4-8: New Firm Installations (Non-Wind) ............................................................................................................................................ 35
Table 4-9: Generator Retirements .................................................................................................................................................................... 36
Table 4-10: Generation Expansion Plan by Province .............................................................................................................................. 38
Table 4-11: Study Scenario Overview, Canada Total .............................................................................................................................. 42
Table 4-12: Scenario Details by Province ...................................................................................................................................................... 43
Table 4-13: Wind Build-out for the USA in all Scenarios ....................................................................................................................... 45
Table 4-14: Wind Plant Aggregation Boundaries ..................................................................................................................................... 47
Table 4-15: Summary Statistics for Grid Cell Aggregation by Province ........................................................................................ 49
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Acronyms and Nomenclatures
Acronyms and Nomenclatures
Base Scenarios
5% BAU
5% Wind Penetration – Business-As-Usual
20% DISP
20% Dispersed Wind Penetration
20% CONC
20% Concentrated Wind Penetration
35% TRGT
35% Targeted Wind Penetration
Unit Types
CC-GAS
Combined Cycle Gas Turbine
COGEN
Cogeneration Plant
DPV
Distributed Photovoltaic
HYDRO
Hydropower / Hydroelectric plant
NUCLEAR
Nuclear Power Plant
OTHER
Includes Biomass, Waste-To-Energy, Etc.
PEAKER
SC-GAS and RE/IC
PSH
Pumped Storage Hydro
PV
Photovoltaic
RE/IC
Reciprocating Engine/Internal Combustion Unit
SC-GAS
Simple Cycle Gas Turbine
SOLAR
Solar Power Plant
ST-COAL
Steam Coal
ST-GAS
Steam Gas
WIND
Wind Power Plant
Canadian Provinces in PCWIS
AB
Alberta
BC
British Columbia
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MB
Manitoba
NB
New Brunswick
ON
Ontario
QC
Quebec
MAR
Maritime
NL
Newfoundland and Labrador
NS
Nova Scotia
PE
Prince Edward Island
SK
Saskatchewan
Acronyms and Nomenclatures
USA Pools in PCWIS
BAS
Basin
CAL
California ISO
DSW
Desert Southwest
FRCC
Florida Reliability Coordinating Council
ISONE
ISO New England
MISO
Midcontinent ISO
NWP
Northwest Power Pool
NYISO
New York ISO
PJM
PJM Interconnection
RMP
Rocky Mountain Pool
SERC-E
SERC Reliability Corporation- East
SERC-N
SERC Reliability Corporation- North
SERC-S
SERC Reliability Corporation- South
SERC-W
SERC Reliability Corporation- West
SPP
Southwest Power Pool Regional Entity
General Glossary
AESO
GE Energy Consulting
Alberta Electric System Operator
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Pan-Canadian Wind Integration Study (PCWIS)
Acronyms and Nomenclatures
BAA
Balancing Area Authority
Btu
British thermal unit
CanWEA
Canadian Wind Energy Association
CF
Capacity Factor
CO2
Carbon Dioxide
DA
Day-Ahead
DNV GL
DNV GL Group
DPV
Distributed PV
DR
Demand Response
EI
Eastern Interconnection
ELCC
Effective Load Carrying Capability
EUE
Expected Un-served Energy
ERGIS
Eastern Renewable Generation Integration Study
EV
Electric Vehicle
EWITS
Eastern Wind Integration and Transmission Study
FERC
Federal Energy Regulatory Commission
FOM
Fixed Operations and Maintenance
GE
GE Energy Consulting
GEII
General Electric International, Inc.
GE EC
GE Energy Consulting
GE MAPS
GE’s “Multi Area Production Simulation” Software
GE MARS
GE’s “Multi Area Reliability Simulation” Software
GE PSLF
GE’s “Positive Sequence Load Flow” Software
GT
Gas Turbine
GW
Gigawatt
GWh
Gigawatt Hour
HA
Hour-Ahead
HR
Heat Rate
IEC
International Electrotechnical Commission
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Acronyms and Nomenclatures
IESO
Independent Electricity System Operator
IPP
Independent Power Producers
IRP
Integrated Resource Planning
kV
Kilovolt
kW
Kilowatt
kWh
Kilowatt Hour
lbs.
Pounds (British Imperial Mass Unit)
LDC
Load Duration Curve
LMP
Locational Marginal Prices
LNR
Load Net of Renewable Energy
LOLE
Loss of Load Expectation
MAE
Mean-Absolute Error
MMBtu
Millions of BTU
MMT
Million Metric Tons
MVA
Megavolt Ampere
MW
Megawatts
MWh
Megawatt Hour
NERC
North American Electric Reliability Corporation
NOX
Nitrogen Oxides
NRCan
Natural Resources Canada
NREL
National Renewable Energy Laboratory
O&M
Operational & Maintenance
PCWIS
Pan-Canadian Wind Integration Study
PPA
Power Purchase Agreement
REC
Renewable Energy Credit
RPS
Renewable Portfolio Standard
RT
Real-Time
RTEP
Regional Transmission Expansion Plan
SCUC
Security Constrained Unit Commitment
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Acronyms and Nomenclatures
SCEC
Security Constrained Economic Dispatch
SO2
Sulfur Dioxide
SOX
Sulfur Oxides
ST
Steam Turbine
TW
Terawatts
TWh
Terawatt Hour
UTC
Coordinated Universal Time
VOC
Variable Operating Cost
VOM
Variable Operations and Maintenance
WECC
Western Electricity Coordinating Council
WI
Western Interconnection
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4
Assumptions and Scenarios
Assumptions and Scenarios
The production cost and reliability modeling conducts a detailed simulation of the North
American power grids and incorporates highly detailed inputs and assumptions for
generators, transmission lines, loads, fuels, and emissions. This section outlines the key
inputs and assumptions required to accurately simulate system operations across Canada.
The underlying data source for most of the inputs and assumptions discussed in this section
was either from the Technical Advisory Committee (TAC) members, Statistics Canada 1, or
from ABB Velocity Suite2. In instances where data was unavailable GE Energy Consulting
utilized engineering judgment and past experience where necessary. The inputs and
assumptions were validated through TAC member review and detailed benchmarking of
model results to historical operations.
4.1 Study Assumptions
4.1.1 Model Footprint
While this study was focused on the Canadian power system, it is critical to accurately
incorporate imports and exports of power between provinces and systems in the United
States. The North American power grids are large interconnected systems and changes in
one region can impact operations in another. In order to capture flows of electricity between
the different balancing areas the modeling incorporated a full nodal representation of the
Eastern and Western Interconnections (two of the three asynchronous power grids, with the
third being the Electric Reliability Council of Texas). Figure 4-1 provides a geographic
representation of the model topology utilized in this study and represents the largest
renewable integration study performed to date.
The Eastern Interconnection (EI) and Western Interconnection (WI) have limited HVDC
interconnections and therefore were modeled as two isolated and separate models. In
addition Quebec’s grid is asynchronous with the rest of the Eastern Interconnection and only
connected through HVDC ties. However, given the large number and size of interconnections
with neighbouring systems, the Quebec system was incorporated directly in the EI model.
The footprint in Canada was again subdivided by balancing area or “pool.” Unlike the United
States, the pool boundaries directly correspond to provincial boundaries. In some cases
inputs and results are aggregated in the Maritimes region. This is consistent with reserve
1 http://www.statcan.gc.ca/start-debut-eng.html
2 http://new.abb.com/enterprise-software/energy-portfolio-management/market-intelligence-services/velocity-suite
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Assumptions and Scenarios
sharing practices between New Brunswick, Nova Scotia and Prince Edward Island as part of
the Northeast Power Coordinating Council (NPCC). While the majority of the reporting in this
study focusses on operations in the Canadian provinces, the simulations were performed for
the whole system.
Figure 4-1: Model Topology of the Eastern and Western Interconnections
Note that the Canadian territories of Yukon, Northwest Territories and Nunavut, as well as
the province of Newfoundland and Labrador were not included in the model topology
because they are composed of isolated power grids and not interconnected to the North
American bulk transmission system. In some cases, existing and proposed power plants
(Churchill Falls and Muskrat Falls) located in Newfoundland and Labrador, but connected via
transmission to Quebec and Nova Scotia, were modeled as generators on the terminal end
of the transmission network. Wind sites and selections were also made in Newfoundland
and Labrador to explore the potential of increasing interconnections to neighbouring
systems.
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Assumptions and Scenarios
4.1.2 Canadian Power System Overview
The Canadian power system is a large, interconnected network composed of nine distinct
grid operators and/or utilities consistent with provincial boundaries. Some provinces are
vertically integrated utilities while others are deregulated ISO/RTO markets. Table 4-1 lists
the grid operator and market structure in each province, listed from west to east.
Table 4-1: List of Provincial Grid Operators and Market Structures
Province
Abbrev
Grid Operator
Market Structure
British Columbia
BC
BC Hydro
Vertically Integrated Utility
Alberta
AB
Alberta Electric System Operator (AESO)
Deregulated ISO/RTO
Saskatchewan
SK
SaskPower
Vertically Integrated Utility
Manitoba
MB
Manitoba Hydro
Vertically Integrated Utility
Ontario
ON
Independent Electric System Operator (IESO)
Deregulated ISO/RTO
Quebec
QC
Hydro Quebec (HQ)
Vertically Integrated Utility
New Brunswick
NB
New Brunswick Power
Vertically Integrated Utility
Nova Scotia
NS
Nova Scotia Power (NSPI)
Vertically Integrated Utility
Prince Edward Island
PEI
Maritime Electric
Vertically Integrated Utility
The resource mix in each province reflects that province’s resource availability, market
structure, and historical development. The British Columbia, Manitoba, and Quebec systems
are predominately hydro based, with over 90% of generation being served by hydro
resources. Alberta, Saskatchewan, New Brunswick and Nova Scotia constitute a mix of coal,
gas, hydro and wind resources. Ontario has a large installed nuclear base, with significant
hydro resources, natural gas capacity, and recent retirement of all coal capacity. Prince
Edward Island load is served predominately from on-island wind and other off-island
generation from New Brunswick. The New Brunswick resource mix also includes the Point
Lepreau Nuclear Generating Station.
Figure 4-2 and Table 4-2 provide the installed capacity by type across each Canadian
province. Note that these figures include new installations and retirements expected
between now and the study year 2025, but do not include any additional wind capacity
added for the scenarios. The chart and table also include thermal and hydro generic
capacity added to systems in order to maintain reserve margin targets due to load growth
between now and the 2025 study year.
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Assumptions and Scenarios
Figure 4-2: Installed Capacity by Type, by Province (2025, without wind additions)
Table 4-2: Installed Capacity by Type (MW), by Province (2025, without wind additions)
BC
AB
SK
MB
ON
NUCLEAR
QC
9,865
COGEN
307
ST-COAL
CC-GAS
211
ST-GAS
10,423
208
7,190
1,247
7,375
6,822
409
15,432
126
2,331
321
2,894
2,624
1,271
6,020
1,970
116
CAN
558
3,476
4,857
MAR
576
PEAKER
90
2,039
649
257
1,123
1,053
1,764
6,975
HYDRO
12,942
523
901
5,891
6,711
41,734
1,893
70,596
OTHER
162
159
65
200
570
152
1,308
SOLAR
490
490
WIND
685
1,438
451
258
4,103
2,960
1,074
10,970
TOTAL
14,397
18,628
5,307
6,532
34,269
46,893
7,626
133,653
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Assumptions and Scenarios
In this study, the installed capacity in 2025 in the “business-as-usual” case (without wind
additions after 2015) is 10,970 MW, with wind plants distributed in each of the Canadian
provinces. Wind resources currently supply approximately 5% of Canada’s annual electricity
demand; and with 36 new wind projects installed in 2015 (1506 MW), wind is the largest
source of new generation capacity in Canada from 2011 to 20153.
4.1.3 General Modeling Assumptions
The following list includes the basic features and assumptions used in the modeling of the
Canadian Power System:

The assumed year of the analysis was 2025, reflecting load energy and peak demand
in 2025 based on the annual growth assumptions for energy; however, the hourly
load shape was based on the historical years of the hourly patterns of the renewable
energy, which for all the base scenarios is based on the year 2008.

All prices and economic inputs and results were quoted in real 2016 Canadian
Dollars, unless otherwise noted.

The United States Dollar (USD) and Canadian Dollar (CAD) exchange rate was set at
1USD:1.385CAD based on the market exchange rate as of January 1st 2016.

Entire Eastern Interconnect and Western Interconnect systems were simulated – a
capability provided by the GE MAPS model.

The Pan-Canadian model spans 4 time zones (Atlantic, Eastern, Central, Mountain,
and Pacific). In order to keep hourly load and wind profiles consistent, the Eastern
Interconnect modeling was conducted in Eastern Standard Time (EST) and the
Western Interconnect modeling was done in Pacific Standard Time (PST). When
chronological inputs or results are shown throughout this report, they are shown in
EST, unless otherwise noted.

Added wind plants were connected to high voltage busses (≥230 kV). This facilitates
the locating of the wind resources in GE MAPS without modeling distribution level
systems and makes the available transmission capacity accessible.

It was assumed that nuclear plants would not cycle to accommodate additional
variable wind energy. This is a conservative assumption, noting that some nuclear
plants in Ontario are already cycling to accommodate additional wind. However, this
cycling is highly situational and subject to many constraints that cannot be modeled
practically.
3 Canadian Wind Energy Association, http://canwea.ca/wind-energy/installed-capacity/
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Assumptions and Scenarios

Existing contingency reserve practices were used in addition to the regulation
reserves calculated to cover the wind and solar variability. Where applicable, the
modeling used the 10-minute spinning reserve portion of the contingency reserve
constraints for each balancing authority.

The production simulation analysis assumed that all units were economically
committed and dispatched while respecting existing and new transmission limits,
generator cycling capabilities, and minimum turndowns, with exceptions made for
any must-run unit or units with operational constraints.

Potential increase in operations and maintenance (O&M) cost of conventional thermal
generators due to increased ramping and cycling were not included.

Renewable energy plant O&M costs were not included. Renewable energy was
considered to be a price-taker.

The hydro modeling did not reflect the specific climatic patterns of 2008, 2009, and
2010, but rather was based on a 10-year long-term average flow per month.
4.1.4 Thermal Generator Modeling
The original source of the thermal generator characteristics was ABB Velocity Suite,
Generating Unit Capacity dataset (accessed on September 26, 2013), and supplemented by
additional data provided by the TAC, where necessary or applicable. The generating thermal
unit modeling included all capacity that was operating, restarted, standby, or under
construction at the time of the data query, including all thermal generators with a capacity
of 3 MW or larger.
Power plants were modeled by individual unit to ensure proper simulation of operation.
Combined cycle gas units were modeled as a single unit, aggregating the gas turbines and
steam turbine into a single generator. Steam turbine and combined cycle generating units
were modeled with multi-block, incremental heat rate curves, whereas gas turbines and
reciprocating engines (quick-start units) were modeled with a single power point. Other
parameters that define thermal plants in GE MAPS include the following:

Primary Fuel: Each unit was assigned to a primary fuel type. Although units may
have dual fuel capability, this study only evaluated a single, primary fuel for each unit.
The fuel assignment is used to calculate total fuel cost and evaluate fuel
consumption.

Max Capacity: The max capacity (MW) represents the maximum amount of power a
given unit can produce in the economic production cost simulations.

Minimum Rating (P-Min Operating): Minimum rating refers to the minimum stable
power output for each unit. The number of MW between the minimum rating and
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maximum rating represents the unit’s operating range. In addition, once a unit is
committed and online, it must operate at least at the minimum rating.

Heat Rate Curves: The incremental heat rate curves provided for each generator are
used to calculate fuel consumption based on loading level.

Variable O&M (VOM): Variable operations and maintenance cost is also modeled
during the production cost optimization. The maintenance cost is dependent on the
unit’s utilization and represents ancillary maintenance costs associated with running
a unit that are accrued when the unit is running. This includes, but is not limited to,
things such as maintenance on turbine parts, water consumption, lubricating oils, etc.

Planned Outage Rate: Planned outage represents the percent of time the generating
unit is unavailable to serve system load in order to conduct planned and scheduled
routine maintenance. These maintenance outages are scheduled optimally by the
model.

Forced Outage Rate: In order to account for unexpected and random generator
outages, each unit is assigned a forced outage rate dictating the amount of time that
the unit is unavailable to produce energy. This outage rate is in addition to any
planned or scheduled maintenance or fixed operating schedules.

Min Down Time & Min Run Time: In order to constrain the operational flexibility of a
unit due to thermal cycling constraints, each generator is assigned a minimum down
time and minimum run time in hours.

Must-run: A unit with the forced commitment (must-run) property must be online at
all times, with the exception of planned and forced maintenance events. When
committed, the units must be producing at or above the unit’s minimum power
rating, regardless of economics. This constraint is included for cogeneration units
which serve a local steam host and sell excess electricity to the grid.

Start-Up Energy: Start-up energy is the amount of fuel consumption required to start
up a unit. If multiplied by the fuel cost, the resulting value represents the total startcost for the unit. This cost is applied every time the unit comes online.
4.1.5 Hydro Generator Modeling
Modeling hydro resources is especially important for the Canadian power system, attributing
more than half of the overall capacity. As a result the study team spent significant effort
developing hydro assumptions for Canadian plants and river systems. The underlying data
source for the hydro modeling efforts was a compilation of sources and the best available
data was assumed based on the hierarchy listed below. The proprietary data shared directly
from TAC was utilized as the primary data source. Other publicly available data by plant and
by province was used as secondary sources where required.
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Assumptions and Scenarios
1. Proprietary data for pondage hydro was provided directly by members of the TAC:
a. BC Hydro provided monthly average generation targets for each of the large
reservoir plants, plus aggregated targets for IPP and small hydro generators.
b. SaskPower provided monthly targets for each plant.
c. IESO provided monthly energy targets for each region (East, Niagara,
Northeast, and Northwest).
d. Manitoba provided monthly energy targets for large dispatchable pondage
hydro, and daily fixed generation targets for each of the run-of-river plants.
e. Hydro Quebec provided monthly energy targets for each plant or plant group.
2. Publicly available historic data is published at plant granularity, accessed via ABB
Velocity Suite, Monthly Plant Generation and Consumption dataset.
a. AESO publicly releases hourly hydro generation by plant. This data was
summarized across multiple years to develop monthly minimum, maximum
and average energy assumptions based on historical operations.
b. New Brunswick releases monthly generation by plant. This data was
summarized across multiple years to develop monthly minimum, maximum
and average energy assumptions based on historical operations.
3. Publicly available historic data, published at provincial granularity, accessed via
Statistics Canada, CANISM dataset, Table 127-0002 Electric Power Generation, by
Class of Electricity Producer, Month (MWh), or other applicable public sources.
While seasonal and annual variation in hydro resources is expected, this study assumed
“normal” hydro operating conditions. The normal hydro conditions were based on historical
average monthly generation and capacity factor profiles from 2003 to 2012, unless normal
conditions were explicitly specified by steering committee members. Figure 4-3 shows an
illustrative example of the monthly and annual variation for Alberta. A similar process was
done for each plant using the underlying best available data source listed above.
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Assumptions and Scenarios
Figure 4-3: Monthly and Annual Hydro Capacity Factor Variation, Alberta Example
In the GE MAPS model each hydro plant is characterized, at a minimum, by the following
information:

Monthly Minimum Hourly Generation (MW): Minimum power plant rating in MW,
which represents any run-of-river portion of the plant, or water flow that must occur
with or without generating power (spillage). The default assumption was 10% of
Monthly Maximum, unless otherwise provided by TAC feedback.

Monthly Maximum (MW): Maximum power plant rating in MW, usually represents the
capacity of the plant, but can be limited by seasonal, environmental, or other factors.
Default assumption was assumed to be winter (October to April) and summer (May to
September) ratings from ABB Velocity Suite, unless otherwise provided by TAC
feedback.

Monthly Energy (MWh): This represents the total available energy that the plant can
produce in the given month. Default assumption was a 10-year average capacity
factor for each month from 2003-2012, CANISM Table 127-00001, unless otherwise
provided by TAC feedback.

Spinning Reserve Capability (% of Up-Range): this number, between 0 and 1,
specifies the percent of unused pondage capacity – or per unit (P.U.) For Spinning
Reserve - that can be used to provide spinning reserve. For example, a 100 MW hydro
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Final Report – Section 4
Pan-Canadian Wind Integration Study (PCWIS)
Assumptions and Scenarios
plant with 0.5 P.U. For Spinning Reserve, running at 60 MW, would have (100 – 60) x
0.5 = 20 MW of spinning reserve capability. If P.U. For Spinning Reserve were, in this
case, 0.1, then the unit will have 4 MW available for spinning reserve. The default
assumption was 1.0, unless otherwise provided by TAC feedback.
Within the bounds of min hourly generation and max hourly generation and the total
monthly energy generation, the dispatch of pondage hydro units is scheduled by the GE
MAPS program against the province’s net load curve (load minus wind and solar generation).
For the base case study scenarios, it was assumed that the scheduling was done against the
day-ahead forecasted wind profiles. This process is illustrated in Figure 4-4, where the hydro
plants would be scheduled against the black dotted line. As a result, the hydro schedules
were coordinated with the forecasted wind resource, but unable to compensate directly
against real-time forecast errors unless previously curtailed surplus energy from the week
was available. This assumption was investigated further through sensitivity analysis. For
some run-of-river hydro plants or resources with significant operational limitations, the
plants were modeled with a fixed hourly profile.
Figure 4-4: Net Load Hydro Scheduling Methodology Example
Additional constraints were modeled for many plants in the Pan-Canadian database. In
general these assumptions were based off of data provided by the TAC or research by the
project team. These constraints were modeled on an as needed or as available basis:

Sequential Dam Logic: By default, the hydro plants were independently scheduled in
an effort to optimally dispatch against system loads. If hydro plants are part of a
hydro system where one plants operation affects another’s, then the sequential dam
logic grouped plants together to coordinate the hydro schedule.

Company, Area, Pool Scheduling: By default, the hydro plants were scheduled
against the pool (provincial) load where they reside. As a result they were only
scheduled against that pool’s unique load shape. However, some hydro plants were
also scheduled against a combined load shape, which aggregated multiple pools or
areas together to form a new composite load profile for scheduling. This was useful
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Final Report – Section 4
Pan-Canadian Wind Integration Study (PCWIS)
Assumptions and Scenarios
for plants in places like Quebec which are used to export into New England, New York,
and Ontario markets, or for plants like Wuskwatim in Manitoba that are used almost
exclusively to serve Midcontinent ISO (MISO) load.

Unavailability: Some hydro resources are unavailable during certain periods (hours,
days, months, etc.) due to resource or environmental constraints.

Scheduling Order: Hydro units were scheduled against the pool loads based on a
preset priority list. By default this list is sorted from largest to smallest, but was
rearranged in some cases based on system operating rules.
4.1.6 Wind Generator Modeling
All wind units were modeled as hourly load modifiers in GE MAPS and follow a pre-defined
hourly generation pattern. Two profiles were modeled for each unit, one forecast profile that
was used during the unit commitment process, and a “real-time” profile that was used
during the dispatch process. The base case assumption utilized a day-ahead wind forecast,
but additional forecast time horizons were evaluated in sensitivity analysis. In the GE MAPS
model, the commitment of thermal units and the hydro scheduling was done off of the
forecasted profiles. Any forecast errors during the dispatch process must be compensated
by surplus up-range on the committed thermal units or by quick-start units (gas turbines,
reciprocating engines, etc.).
Wind resources were assumed to have zero fuel and O&M costs, and hence are assumed to
be available at no cost in the dispatch stack. The model does not take into account any
power purchase agreement (PPA) based prices of independent power producers (IPP) in
dispatch of wind and solar resources. However payments to IPPs can be post-processed.
The hourly wind profiles used throughout the study are discussed in detail throughout
Section 4.3, and represent modeled wind generation patterns based on meteorological data
from the years 2008, 2009, and 2010. The year 2008 was the default assumption for wind
and load profiles, with other years evaluated in sensitivity analysis. Each wind plant has a
unique production profile based on its geographic location and scaled according to the MW
rating of the plant.
It is important to distinguish between the available generation profiles (GE MAPS inputs) and
the actual dispatched generation profiles (GE MAPS outputs). The hourly dispatched
generation is an output from the GE MAPS algorithm that takes into account any necessary
curtailment. Wind generation are the last resources to be curtailed (i.e., spilled) during the
low load and high supply periods. In such times, GE MAPS uses a priority order, whereby the
more expensive thermal unit operations are reduced, but only up to their minimum load
(they are still kept online if already committed). If no more thermal generation is available for
backing down, then GE MAPS uses an assigned priority order to curtail the remaining wind
and hydro resources. The last in the priority order is typically non-grid scale distributed solar
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Final Report – Section 4
Pan-Canadian Wind Integration Study (PCWIS)
Assumptions and Scenarios
generation, assumed to be not responsive to system operators’ curtailment commands.
Another important curtailment input was that the study assumed nuclear units would not
decrease generation to accommodate additional wind energy.
4.1.7 Curtailment
Curtailment refers to the reduction of generation from renewable resources below the levels
available in the underlying resource. For example, if the wind resource is able to produce 100
MW of generation, but the system operators dispatch the plant at 60 MW, there is 40 MW of
curtailed, or unused, power. There are several reasons why a system operator may choose
to curtail a renewable resource, including transmission congestion, grid stability or reliability
concerns, ramp rate or cycling constraints of other generators, environmental constraints, or
other engineering, economic, or system constraints. The curtailed energy represents an
opportunity cost, because absent storage, the energy is wasted and must be supplied by
other sources.
Throughout this study, curtailment includes unused wind, solar, and hydro resources and are
treated equivalently for reporting purposes. The system operator’s decision of which
resource, or individual plant, to curtail is based on different environmental, economic,
contractual, and engineering considerations, but the net effect is the same – the grid is
unable to accommodate a zero marginal cost resource. The curtailed energy is wasted and
must be provided by other resources. The alternate resources may have higher operating
costs and thereby lead to reduced system economic efficiency. As a result the project
reporting does not differentiate between different types of resource curtailment. For the
sake of modeling assumptions, it was assumed that new wind additions (absent other
constraints) were curtailed before the existing hydro and solar plants, because in this study
they represent the agent of change and incremental additions to the system. This
curtailment order is a practical assumption for this study, but is not intended to represent
existing operational practices or a recommended future practice.
In some cases, pondage hydro resources have the ability to store curtailed or unused
energy. The amount of storage available depends on the size of reservoir, along with
environmental and societal limitations. While the model did assume some curtailed energy
could be carried forward for relatively short periods of time (days), this study did not analyze
the ability to shift energy across seasons or years. This modeling decision was made with
support of the Technical Advisory Committee, with an expectation that longer-term hydro
storage would likely be evaluated in subsequent studies.
Options for reducing curtailment to lower levels include:

Additional transmission infrastructure, which would relieve congestion and enable
access to load centers by more renewable energy. The optimum level of transmission
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Final Report – Section 4
Pan-Canadian Wind Integration Study (PCWIS)
Assumptions and Scenarios
reinforcements would depend on the value of additional recovered renewable energy
versus cost of additional transmission.

Shifting of hydro energy usage, with hydro pondage acting as storage of potentially
curtailable energy by reducing hydro generation and shifting discharge by hours,
days, weeks, months, or seasons. This would involve changing the monthly hydro
energy dispatch schedules to be more compatible with short-term variability as well
as seasonal patterns in wind generation. Several Canadian provinces have large
hydro resources with long-term pondage, so this option for mitigating curtailment
offers significant opportunity to reduce energy curtailment with higher penetration of
wind power.

Scheduling hydro resources against real-time wind and load, which assumes that
hydro resources are more flexible than the Base Case assumption in the study, which
assumes hydro resources are scheduled against net load and the day-ahead wind
forecast. This option was considered and is reported as a sensitivity analysis in this
study.

Providing more operational flexibility in thermal generation, such as increasing ramp
rates, decreasing unit minimum run time and down time, and lowering the minimum
operating load of units.
4.1.8 Fuel Price Projections
4.1.8.1 Natural Gas Price Assumptions
The natural gas price assumption is one of the most important economic variables in the
model. This is because the marginal generator on the system is typically fueled by natural
gas and thus represents the fuel displaced by wind. This is true even for Canadian regions
with limited gas consumption because the USA export market is still based on natural gas as
the marginal fuel.
Monthly natural gas prices are based on the Henry Hub prices from the EIA Annual Energy
Outlook 2014 Report4. Delivered prices across the Canadian regions provide the additional
“basis differentials” reflecting the time and location dependent variations in the cost of
natural gas. The basis differentials are the 2008-2013 average monthly differentials relative
to Henry Hub and sourced from Enerfax historical data, accessed via ABB Velocity Suite.
The delivered natural gas prices for each province are provided in Figure 4-5 and Table 4-3
for each province and each month. Prices are quoted in C$/GJ, assuming a conversion
factor of 0.947 MMBtu per GJ. As the table and chart illustrate, prices are, in general, lowest
4 http://www.eia.gov/forecasts/aeo/pdf/0383(2014).pdf
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Final Report – Section 4
Pan-Canadian Wind Integration Study (PCWIS)
Assumptions and Scenarios
in Alberta and highest in the Maritimes region at the extremity of the pipeline network. The
underlying seasonality (higher prices in winter) also coincides with peak demand for both
electricity and gas heating demand.
Figure 4-5: 2025 Natural Gas Price Assumptions by Pricing Node (2016 C$/GJ)
Table 4-3: 2025 Natural Gas Price Assumptions by Pricing Node (2016 C$/GJ)
BC
AB
SK
MB
ON
QC
NB
NS
NWP
Sumas
AB NIT
(AECO)
AB NIT
(AECO)
Alliance
Delivered
Dawn
Hub
Iroquois
(Into)
Iroquois
(Into)
Iroquois
(Into)
JAN
7.64
7.03
7.03
7.95
8.14
9.90
9.90
9.90
FEB
7.19
6.55
6.55
7.47
7.50
9.11
9.11
9.11
MAR
6.83
6.16
6.16
7.03
5.92
8.02
8.02
8.02
APR
6.93
6.07
6.07
6.97
5.80
7.72
7.72
7.72
MAY
7.14
6.37
6.37
7.45
7.76
8.01
8.01
8.01
JUN
7.37
6.63
6.63
7.70
7.97
8.22
8.22
8.22
JUL
6.96
6.38
6.38
7.47
7.77
8.13
8.13
8.13
AUG
5.91
5.59
5.59
6.71
6.98
7.23
7.23
7.23
SEP
5.90
5.37
5.37
6.34
6.71
6.91
6.91
6.91
OCT
6.48
6.22
6.22
6.81
6.95
7.26
7.26
7.26
NOV
6.03
6.29
6.29
6.74
6.98
7.76
7.76
7.76
DEC
7.08
7.09
7.09
7.55
7.74
9.52
9.52
9.52
AVG
6.79
6.31
6.31
7.18
7.18
8.15
8.15
8.15
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Pan-Canadian Wind Integration Study (PCWIS)
Assumptions and Scenarios
4.1.8.2 Other Fuel Price Assumptions
The assumed fuel prices for coal, oil, uranium and biomass/other/waste, etc. are provided in
Table 4-4 for the 2025 simulation year. The underlying data source for the coal prices is
based on TAC feedback in Alberta and Saskatchewan. This data was supplemented for New
Brunswick and Nova Scotia by using on an average of delivered coal price from EIA Annual
Energy Outlook for New England. The oil prices are also based on the EIA 2014 Annual
Energy Outlook. Since the start of the study, prices in global oil markets have decreased
considerably. However, given that oil based generation is a very small portion of the overall
generation mix, the oil price assumption will only have a marginal impact on the study
results.
Table 4-4: 2025 Coal, Oil, Uranium and Other Fuel Price Assumptions (2016 C$/GJ)
2025 Price
(2016 C$/GJ)
Fuel Type
Data Source
AB Coal
AESO
$2.40
SK Coal
SaskPower
$2.14
NB Coal
Assumed from US Data (average of ISONE)
$6.40
NS Coal
Assumed from US Data (average of ISONE)
$6.40
Oil (distillate)
EIA 2014 Annual Energy Outlook
$28.79
Oil (residual)
EIA 2014 Annual Energy Outlook
$19.20
Uranium
GE Energy Consulting
$1.10
Biomass/Other
GE Energy Consulting
$1.10
4.1.9 Load Projections
4.1.9.1 Annual Energy and Peak Demand Forecast
The demand forecast used throughout the study is used for two purposes; it is used during
the production cost and reliability simulations and it determines the amount of wind
penetration assumed in each scenario. For example, a 20% wind penetration assumes that
20% of the annual load energy is served by wind energy. Therefore a higher load forecast
will yield further wind capacity additions.
A 2025 load forecast of the annual energy (GWh) and peak demand (MW) was used
throughout the model footprint. The load projections were based on the 2013 NERC Long
Term Reliability Assessment5 for both the United States and Canada, unless data was
5 http://www.nerc.com/pa/RAPA/ra/Reliability%20Assessments%20DL/2013_LTRA_FINAL.pdf
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Final Report – Section 4
Pan-Canadian Wind Integration Study (PCWIS)
Assumptions and Scenarios
supplemented by input provided by the TAC members (BC Hydro, AESO, and IESO). Table 4-5
provides the annual load energy (GWh), peak demand (MW), and load factor for each
Canadian province in the model footprint for the forecast year 2025. Load factor is defined
as the annual energy divided by the product of the peak demand and the number of hours in
the year.
Table 4-5: 2025 Load Forecast by Province
BC
Annual
Energy
(GWh)
63,433
Peak
Demand
(MW)
11,622
AB
116,234
16,318
0.81
SK
29,626
4,444
0.76
MB
30,149
5,261
0.65
ON
143,670
24,358
0.67
QC
200,736
41,171
0.56
NB
12,780
2,973
0.49
NS
11,904
2,176
0.62
PEI
1,086
241
0.51
609,618
108,564
0.64
CAN*
Load
Factor
(%)
0.62
*Total peak demand is non-coincident
4.1.9.2 Chronological Load Patterns
The annual energy and peak demand targets shown in Table 4-5 are used to scale the
hourly chronological loads for each province. The chronological load patterns were based off
of historical load data, accessed via ABB Velocity Suite’s Historical Demand by Zone Hourly
dataset. In order to maintain weather-linked correlation between historical load and wind
profiles the 2008 weather year load profile was scaled up to the annual energy and peak
demand targets by the GE MAPS model. This process was repeated for 2009 and 2010
weather years in the sensitivity analyses. The hourly data is summarized by month for the
Pan-Canadian system is provided in Figure 4-6.
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Final Report – Section 4
Pan-Canadian Wind Integration Study (PCWIS)
Assumptions and Scenarios
Figure 4-6: 2025 Monthly Load Energy and Peak Demand for Canada
4.1.10 Transmission
4.1.10.1
Transmission Constraints and Interface Definitions
The GE MAPS production cost simulation included a full transmission representation of the
Eastern Interconnect (MMWG load flow) and Western Interconnect (TEPPC load flow),
including a full configuration of the transmission grid including all the major transmission
lines and transmission system buses. All load busses were assigned to the appropriate GE
MAPS areas and corresponding load forecast and all generating units were assigned to the
correct generation bus. The solved load flow is used to create the generation shift factor
(GSF) matrix to determine the transmission flows of generation and loads across the
network. A map of the high voltage transmission network across Canada is provided in
Figure 4-7.
Figure 4-7: High Voltage Transmission Network Map of Canada
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Final Report – Section 4
Pan-Canadian Wind Integration Study (PCWIS)
Assumptions and Scenarios
Based on data availability, the production cost modeling did not include a full representation
of all transmission constraints across the Pan-Canadian system. Instead the model included
major transmission constraints between each province and neighbouring systems (intraprovincial) in both Canada and USA. In Ontario and Nova Scotia additional inter-provincial
constraints were also added to the model to represent historical transmission congested
interfaces. The transmission interface definitions assumed existing operating constraints
used throughout planning studies in Canada and are provided in Table 4-6, Table 4-7, and
Figure 4-8. The interfaces include additional transmission lines that are currently in
advanced stages of development or under construction and listed below. These added
transmission lines were assumed exogenously based in input provided by the TAC members,
and not part of the transmission expansion methodology discussed in later sections of this
report.



Alberta to British Columbia: Increased existing interconnection capacity to 1,200
MW based on TAC feedback regarding plans to increase the WECC Path 1 Rating in
the future.
Manitoba to Minnesota: Included the new 500 kV transmission project currently
under construction.
Quebec to United States: Included three proposed HVDC projects from Quebec to
New York (Champlain Hudson Power Express, 1,000 MW) and New England (Northern
Pass, 1,200 MW and New England Clean Power Link, 1,000 MW)
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Final Report – Section 4
Pan-Canadian Wind Integration Study (PCWIS)
Assumptions and Scenarios
Table 4-6: Inter-Provincial Transmission Interface Limits
From Side:
Canada Province
British Columbia
Saskatchewan
Ontario
New Brunswick
GE Energy Consulting
Inter-Area Tie Branch
From Bus
Rainbow Lake
Fording Coal
Cranbrook
Natal
Swift Current
Island Falls
Island Falls
E B Campbell
Yorkton
Bounpary Dam
Kenora
Kenora
Kenora
Hawthorne
Hawthorne
Madawaska
Eel River
Eel River
Salisbury
Memramcook
Memramcook
Murray Corner
Murray Corner
To Bus
Fort Nelson
Pocaterra
Langdon
Coleman
McNeill
Flin Flon
Flin Flon
Pas Ralls Island
Roblin
Reston
Whiteshell
Whiteshell
Seven Sisters
Outaouais
Outaouais
Riviere du Loup
Matapedia
Matapedia
Onslow
Maccan
Maccan
Borden
Borden
CKT
1
1
1
1
1
1
2
1
1
1
1
2
1
1
2
1
1
2
1
1
2
1
2
32
kV
138
138
500
138
230
115
115
230
230
230
220
220
115
230
230
230
230
230
345
138
138
138
138
Limit (MW)
From->To To->From
SP WP SP WP
-
To Side:
Canada Province
DC
AC
AC
Alberta
AC 1200 1200 1200 1200
AC
DC 150 150 150 150
AC
AC
AC
0
150 150
AC 0
Manitoba
AC
AC
AC 288 300 288 300
AC
DC
1250 1250 1250 1250
DC
Quebec
DC
DC 785 785 1029 1029
DC
AC
Nova Scotia
AC 300 300 350 300
AC
DC
200 200 200 200 Prince Edward Island
DC
Final Report – Section 4
Pan-Canadian Wind Integration Study (PCWIS)
Assumptions and Scenarios
Table 4-7: International Transmission Interface Limits between Canada and USA
From Side:
Canada Province
British Comlubia
Alberta
Saskatchewan
Manitoba
Ontario
Quebec
New Brunswick
GE Energy Consulting
Limit (MW)
From->To
To->From
SP
WP
SP
WP
Inter-Area Tie Branch
From Bus
Ingledow
Ingledow
Nelway
Waneta
Marias
Boundary Dam
Glenboro
Letellier
Dorsey
Dorsey
Richer South
Fort Francis
Keith
Scott
Lambton
Lambton
St. Lawrence
St. Lawrence
Beck 2BP76
Beck 2PA27
Beck A
Beck B
Les Cedres
Les Cedres
Chateauguay
Chateauguay
HER735
HER735
Standstead
Bedford
Canton
Nicolet
Nicolet
Keswick
Point Lepreau
To Bus
CKT kV DC
Custer
1 500 AC
Custer
2 500 AC
3150
Boundary
1 230 AC
Boundary
1 230 AC
MATL
1 230 AC 315
Tioga
1 230 AC 165
Rugby
1 230 AC
Drayton
1 230 AC
Forbes
1 500 AC 2833
Blackberry
1 500 AC
Moranville
1 230 AC
International Falls 1 115 AC 150
Waterman
1 230 AC
Bunce Creek
1 230 AC
1700
St. Clair
1 230 AC
St. Clair
1 345 AC
Moses
1 230 AC
300
Moses
2 230 AC
Packard
1 230 AC
Moses Niagara
1 230 AC
1760
Moses Niagara
1 345 AC
Moses Niagara
1 345 AC
Dennison
1 115 DC
199
Dennison
2 115 DC
Massinna
1 765 DC
1500
Massinna
2 765 DC
Astoria
1 765 DC 1000
Coolidge
1 765 DC 1000
Derby
1 115 AC 35
Highgate
1 115 DC 225
Deerfield
1 765 DC 1200
Sandy Pond
1 765 DC
1700
Sandy Pond
2 765 DC
Keane Road
1 345 AC
700
Orrington
1 345 AC
33
To Side:
US State
3150
3000
3000
Washington
315
165
310
150
310
150
Montana
North Dakota
2833
1400
1400
Minnesota
150
100
100
1750
1550
1550
300
300
300
2090
1320
1570
Michigan
New York
180
100
100
1500
1000
1000
1000
1000
35
200
1200
1000
1000
0
170
1200
1000
1000
0
170
1200
New Hampshire
1700
1700
0
Massachusetts
700
500
500
Maine
Vermont
Final Report – Section 4
Pan-Canadian Wind Integration Study (PCWIS)
Assumptions and Scenarios
Figure 4-8: IESO Intra-Provincial Transmission Interfaces
4.1.10.2
Hurdle Rates
In addition to the transmission constraints listed above, the model included economic
“hurdle rates” that place an economic charge on transfers between operating areas. This is
used to simulate both the wheeling charges between balancing areas and market “friction”
that may result from different operating rules and procedures in different utilities. It was
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Final Report – Section 4
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Assumptions and Scenarios
assumed that the hurdle rate between balancing areas (across both USA and Canada) was
C$5/MWh during the commitment process and C$3/MWh during the dispatch process.
4.1.11 Generation Expansion Methodology
The GE MAPS production cost and GE MARS reliability models were also updated to
incorporate changes in the supply mix to reflect the North American grid in the year 2025.
This process incorporated public announcements of new installations and retirements as
well as generic expansion generators required to maintain reserve margin adequacy.
4.1.11.1
New Installations and Retirement Assumptions
The model included any units that had a unit status of under construction, site-prep, and/or
testing along with planned and proposed plant retirements. The primary data source for the
installations and retirements data was the ABB Velocity Suite, Generating Unit Capacity
dataset as of January 1st, 2014. In addition, specific proposed installations and retirements
were added based on TAC member suggestions. The study assumptions also retired coal
plants that reach the end of their useful life based on federal coal regulation (>=50 years old
or build before 1975) before the study year of 2025. Note that since the start of the study,
some provinces may have changed the coal retirement timeline (most notably Alberta), but
this new policy was not reflected in the base case assumptions. Instead it was evaluated as
a sensitivity analysis. The list of new generator installations and generator retirements are
provided in Table 4-8 and Table 4-9.
Table 4-8: New Firm Installations (Non-Wind)
Plant Name
Site C Hydro
Conifex MacKenzie Biomass
Cold Lake Nabiye GT
Mustus Biomass
Kearl Oil Sands Project
Shephard Energy Center
Queen Elizabeth Exp. CC
La Romaine Hydro
Muskrat Falls (Through Maritime Link)
GE Energy Consulting
35
Province
Capacity (MW)
BC
BC
AB
AB
AB
AB
SK
QC
NS
1,090
36
170
42
100
821
205
1,305
500
Final Report – Section 4
Pan-Canadian Wind Integration Study (PCWIS)
Assumptions and Scenarios
Table 4-9: Generator Retirements
Plant Name
Province
Capacity
(MW)
BC
AB
AB
AB
SK
SK
MB
NB
NS
NS
900
690
144
560
339
78
97
300
306
154
Burrard Thermal STs
Battle River Coal Units 3, 4, 5
H.R. Milner Coal
Sundance Coal Units 1, 2
Boundary Dam Coal Units 2, 4, 56
Landis GT7
Brandon Coal Unit 5
Dalhousie ST Units 1, 2
Lingan Coal Units 1, 2
PT Tupper Coal
4.1.11.2
Thermal Generation Expansion Planning Process
In order to ensure that the system has enough capacity to maintain reliability given the
expected load growth, the following generation expansion methodology was used. A
thermal generation expansion plan used in all study scenarios, including those with higher
penetration of wind, was developed based on the load and capacity assumptions used in the
first scenario, without any additional wind capacity additions. The expansion plan was then
held constant across the scenarios evaluated, regardless of the firm capacity benefits
provided by the incremental wind additions. The consistent thermal expansion plan was
used to ensure that all changes between the scenarios could be attributed only to the
addition of wind energy and avoid adding additional changes to the system that could
impact results.
The amount of generation capacity to add was based on the installed reserve margin (RM) in
each pool. It was determined that the reserve margin in each pool should be at or exceed
the reserve margin target listed in the 2013 NERC Long Term Reliability Assessment. If the
given load growth, new installation, and retirement forecast resulted in a reserve margin
deficit, then generic expansion units were added to the model so that the reserve margin
target was achieved. The equation for the reserve margin calculation is provided below:
6 According to communication from SaskPower, final decision on the retirement or conversion to carbon capture and
storage for units BD4 and BD5 has not been made. BD2 has been retired.
7 According to communication from SaskPower, final decision on the retirement of Landis Power Station has not been
made.
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𝑹𝒆𝒔𝒆𝒓𝒗𝒆 𝑴𝒂𝒓𝒈𝒊𝒏 =
Assumptions and Scenarios
[𝑸𝒖𝒂𝒍𝒊𝒇𝒊𝒆𝒅 𝑪𝒂𝒑𝒂𝒄𝒊𝒕𝒚 + 𝑭𝒊𝒓𝒎 𝑵𝒆𝒕 𝑰𝒎𝒑𝒐𝒓𝒕𝒔 − 𝑫𝑺𝑴]
𝑷𝒆𝒂𝒌 𝑫𝒆𝒎𝒂𝒏𝒅
Where:

Qualified Capacity is the firm capacity value of generation resources.

Firm Net Imports is the net (Imports – exports)

DSM is the Demand Side Management resources

Peak Demand is the annual peak demand for the year under consideration.
For wind and hydro resources the firm capacity may be lower than the nameplate capacity
due to resource availability during peak time periods. It should be noted that the capacity
value for wind resources used in this part of the analysis was the existing firm capacity value
used by each province and not the capacity values calculated later in this report.
In cases where the reserve margin fell below target levels, additional units were added to the
model based on the following methodology, the results of which are provided in Table 4-10.
While this is not intended to be an optimal expansion plan, it is sufficient to balance the
system and for use in a wind integration study.
•
Two main types of generic Candidate Plants were selected:
o A future Combined Cycle Natural Gas Turbine (CC-GAS) Type, rated at 500 MW
with an assumed heat rate of 6,800 b Btu/KWh
o A future Single Cycle Natural Gas Turbine (SC-GAS) Type, rated at 200 MW with
an assumed heat rate of 10,800 Btu/KWh
o Although firm capacity was added in the form of hydro natural gas-fueled
generation, there can be other, less emitting forms of firm capacity that could
be considered – e.g., contractual imports, energy storage, demand response,
etc.
o Note: British Columbia, Manitoba and Quebec TAC Members suggested that
the model should not include any future thermal generation, but instead use
hydro resources for capacity expansion. In addition, capacity additions were
not required for those regions.
•
In 5% BAU Scenario, added an initial set of CC-GASs & SC-GASs to meet annual
reserve margin target.
•
Ran GE MAPS iteratively to refine SC-GAS and CC-GAS mix to quantify the expected
utilization of the capacity additions. The technology choice (SC-GAS vs. CC-GAS) was
based on a utilization threshold of >30% for CC-GAS units and <10% for SC-GAS units.
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Assumptions and Scenarios
If the resulting utilization from the GE MAPS simulation was outside of those
constraints, the technology choice was switched.
Table 4-10: Generation Expansion Plan by Province
Hydro Firm
Capacity (%)
Wind Firm
Capacity (%)
Unbalanced
RM (%)
Target
RM (%)
Generic CCGAS Add
(MW)
Generic SCGAS Add
(MW)
BC
87%
21%
23%
16%
0
0
AB
67%
20%
-17%
12%
4,000
800
SK
100%
20%
1%
11%
500
200
MB
100%
0%
20%
12%
0
0
ON
72%
13%
8%
20%
2,500
600
QC
96%
28%
11%
10%
0
0
MAR
100%
31%
20%
20%
0
0
4.2 Study Scenarios
4.2.1 Selected Scenarios
The PCWIS evaluated four main scenarios in an effort to understand the operational and grid
impacts of increased wind energy across Canada. The scenarios were selected to provide
insight on both the magnitude and location of wind expansion. The level of wind penetration
ranged from approximately existing 2016 levels (5%) up to 35% of annual load energy
(nationally) in the highest scenario. The locations of wind additions also varied, with some
scenarios having dispersed wind across Canadian provinces, while other scenarios
concentrated wind to the best resource locations or regions where displacement of thermal
generation (and therefore emissions reductions) could be maximized. The four scenarios
evaluated throughout the study are:
•
5% Business-as-Usual Scenario (5% BAU): The 5% BAU Scenario represents an
approximation of the Canadian power system and includes all wind plants in Canada
that were operating or under construction as of 4/25/2015. Each wind plant was
assigned to the nearest wind profile site and the output was scaled to align with the
current plant capacity (while assuming state of the art turbine technology to be
consistent with other scenarios).
•
20% Dispersed Scenario (20% DISP): The 20% DISP Scenario represents the
Canadian power system with enough wind energy available to serve 20% of the
annual load energy in each province. Wind sites were selected, incrementally to the
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Pan-Canadian Wind Integration Study (PCWIS)
Assumptions and Scenarios
sites already included in the 5% BAU Scenario, so that each province had enough
wind locally to serve 20% of the annual provincial load. Incremental sites were
selected based on the best available resources within each province, while
accounting for distance to nearest high voltage transmission. As a result, wind site
selection was dispersed across Canada, in proportion to the load in each province.
•
20% Concentrated Scenario (20% CONC): Similar to the 20% DISP Scenario, the 20%
CONC Scenario represents the Canadian power system with enough wind energy
available to serve 20% of the annual load energy across Canada. The site selections
were incremental to the 5% BAU Scenario. As a result, the annual available energy is
the same as the 20% DISP scenario, but the 20% CONC scenario concentrated the
wind site location in regions with the best wind resources and therefore less installed
wind capacity. This is the only scenario where wind sites were allowed to be selected
in Newfoundland and Labrador, based on the quality of the wind resource in the
province. Wind sites were selected based on capacity factor and distance to
transmission only, irrespective of the provincial load energy.
In addition, the 20% CONC scenario included additional site selection criteria to limit
the geographic concentration of wind sites. The additional criteria included:
o Minimum penetration limit of 10% annual energy penetration for each
province (applied to British Columbia, Saskatchewan, and Quebec)
o Maximum penetration limit of 50% (applied to Nova Scotia)
o A selection of at least one additional site in each province relative to the 5%
BAU Scenario.
o In Alberta, to avoid an over concentration of wind expansion exclusively in the
southern region of the province, some wind locations were manually adjusted
to a to 70%/30% split by adding wind to more northern Red Deer area.
•
35% Targeted Scenario (35% TRGT): 35% TRGT Scenario represents the Canadian
power system with enough wind energy available to serve 35% of the annual load
energy across Canada, with wind locations targeted to achieve thermal generation
displacement, emissions reduction in Canada. This scenario was developed after
preliminary review of the first three scenarios. The starting point of the scenario was
the 20% DISP scenario included all of the 5% BAU and 20% DISP sites and added new
incremental wind sites proportional to each province’s thermal generation in the 5%
BAU case. As a result, new wind sites were targeted to Alberta, Saskatchewan,
Ontario, and the Maritimes regions.
In addition, the 20% CONC scenario included additional site selection criteria to limit
the geographic concentration of wind sites. The additional criteria included:
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Pan-Canadian Wind Integration Study (PCWIS)
Assumptions and Scenarios
o Minimum penetration limit of 25% annual energy penetration for each
province (applied to British Columbia, Saskatchewan, and Quebec)
o Maximum penetration limit of 50% (applied to Alberta, Saskatchewan, New
Brunswick, and Nova Scotia)
o In Alberta, to avoid an over concentration of wind expansion exclusively in the
southern region of the province, some wind locations were manually adjusted
to a to 70%/30% split by adding wind to more northern Red Deer area.
o No additional sites were selected in the Bruce Peninsula region of Ontario and
the Gaspe Peninsula region of Quebec in an effort to increase geographic
diversity and over correlation of wind output.
A map showing the geographic locations of the wind plants selected in each scenario is
provided in Figure 4-9, followed by additional summaries.
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Pan-Canadian Wind Integration Study (PCWIS)
Assumptions and Scenarios
5% BAU Scenario
Map ©2015 Google
Existing Sites
20% DISP Scenario
Map ©2015 Google
Existing Sites
20% DISP Sites
20% CONC Scenario
Map ©2015 Google
Existing Sites
20% CONC Sites
35% TRGT Scenario
Map ©2015 Google
Existing Sites
35% TRGT Sites
Figure 4-9: Locations of Selected Wind Plants by Study Scenario
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Pan-Canadian Wind Integration Study (PCWIS)
Assumptions and Scenarios
A summary of the Scenario Development is provided in Figure 4-10 and Table 4-11, with
additional details provided for each province in Table 4-12, Figure 4-11, and Figure 4-12.
These tables and figures provide a detailed overview of the amount of wind capacity (MW),
energy (GWh), and quality of wind resource in each region (capacity factor %). Note that in
the 20% DISP scenario the annual penetration is slightly higher than 20% CONC for Canada
because Prince Edward Island already has more than 20% annual penetration. In addition,
the wind energy, capacity factor, and penetration values presented in this section represent
available wind energy and do not take into account potential curtailment which was
addressed in the production cost modeling.
Figure 4-10: Study Scenario Overview
Table 4-11: Study Scenario Overview, Canada Total
Scenario
Wind
Penetration
Level (%)
Number of
Wind Sites
Used
Wind
Capacity
(MW)
Wind
Energy
(GWh)
Average
Capacity
Factor (%)
5% BAU
5.7%
116
10,970
34,717
36.1%
20% DISP
20%
229
37,131
122,054
37.5%
20% CONC
20%
220
36,311
121,584
38.2%
35% TRGT
35%
333
65,225
212,734
37.2%
Note: Totals and capacity factors may not match due to rounding.
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Assumptions and Scenarios
Table 4-12: Scenario Details by Province
BC
AB
SK
Wind Capacity (MW)
5% BAU
685
1,438
451
20% DISP
4,270
6,944
1,749
20% CONC
2,221
9,840
915
35% TRGT
5,445
17,728
4,407
Available Wind Energy (GWh)
5% BAU
1,751
4,527
1,471
20% DISP
12,592
23,148
5,923
20% CONC
6,520
32,874
3,077
35% TRGT
15,734
57,879
14,804
Available Wind Capacity Factor (%)
5% BAU
29.2%
35.9%
37.2%
20% DISP
33.7%
38.1%
38.7%
20% CONC
33.5%
38.1%
38.4%
33.0%
37.3%
38.3%
35% TRGT
Available Wind Penetration (% of Load)
5% BAU
2.8%
3.9%
5.0%
20% DISP
20%
20%
20%
20% CONC
10%
28%
10%
35% TRGT
25%
50%
50%
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MB
ON
QC
NB
PEI
NS
NL
CAN
258
1,781
2,789
2,213
4,103
8,440
10,056
16,124
2,960
12,275
6,128
15,490
484
796
796
1,967
201
201
201
201
390
675
1,587
1,651
0
0
1,776
0
10,970
37,131
36,311
65,225
859
6,008
9,495
7,502
13,610
28,640
34,162
53,651
9,074
40,118
20,100
50,128
1,479
2,559
2,556
6,397
686
686
686
686
1,261
2,381
5,782
5,952
0
0
6,332
0
34,717
122,054
121,584
212,734
38.0%
38.5%
38.9%
38.7%
37.9%
38.7%
38.8%
38.0%
35.0%
37.3%
37.4%
36.9%
34.9%
36.7%
36.7%
37.1%
39.0%
39.0%
39.0%
39.0%
36.9%
40.3%
41.6%
41.2%
2.9%
20%
32%
25%
9.5%
20%
24%
37%
4.5%
20%
10%
25%
11.6%
20%
20%
50%
63%
63%
63%
63%
10.6%
20%
49%
50%
Final Report – Section 4
40.7%
N/A
N/A
N/A
N/A
36.1%
37.5%
38.2%
37.2%
5.7%
20%
20%
35%
Pan-Canadian Wind Integration Study (PCWIS)
Assumptions and Scenarios
Figure 4-11: Installed Wind Capacity by Scenario, by Province
Figure 4-12: Average Available Capacity Factor by Scenario, by Province
4.2.2 Wind Additions in the United States
With significant amount of interconnection and power flows between the United States and
Canada, it was important to include wind expansion in the USA power systems as well. Each
scenario included a build out of wind capacity in the USA to achieve full compliance from
state renewable portfolio standard (RPS) requirements. To do this the study team leveraged
scenarios and wind profiles developed for previous studies lead by the USA Department of
Energy National Renewable Energy Laboratory (NREL), including the Eastern Renewable
Generation Integration Study (ERGIS) and the Western Wind and Solar Integration Study
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Assumptions and Scenarios
Phase 2 (WWSIS2) studies. As a result, no new analysis for the wind resource, hourly profiles,
or site selection was conducted by the project team for this study. The USA portions of the
GE MAPS database were modified to incorporate the NREL wind capacity additions and
hourly profiles for the year 2008. In addition, the same sub-hourly regulation reserve
requirements from the NREL studies were used in the USA power pools.
The wind capacity and available wind energy in the USA remained unchanged throughout
the Scenarios. This was done to ensure that any changes taking place on the power system
were a direct result of the additional wind installations evaluated in each of the four
scenarios. However, a sensitivity analysis was conducted to evaluate the impact of a 20%
increase in wind energy availability in the USA system. Table 4-13 provides an overview of
the wind build-out in the USA across all scenarios.
Table 4-13: Wind Build-out for the USA in all Scenarios
Annual
Load
(GWh)
BAS
CAL
DSW
FRCC
ISONE
MISO
NWP
NYISO
PJM
RMP
SERC-E
SERC-N
SERC-S
SERC-W
SPP
TOTAL USA
87,598
332,500
167,059
259,363
133,902
636,222
193,991
173,294
969,027
78,160
255,709
249,537
293,229
148,672
288,431
4,266,695
Wind
Capacity
(MW)
Available
Wind
Energy
(GWh)
995
7,299
4,174
0
5,218
40,343
10,392
12,076
15,630
5,040
3,760
200
0
0
28,927
134,054
2,975
23,212
12,254
0
19,016
156,898
32,875
43,130
55,680
18,483
14,098
467
0
0
118,873
497,960
Available
Capacity
Factor
(%)
Available
Wind
Penetration
(%)
34%
36%
34%
0%
42%
44%
36%
41%
41%
42%
43%
27%
0%
0%
47%
42%
3%
7%
7%
0%
14%
25%
17%
25%
6%
24%
6%
0%
0%
0%
41%
12%
4.3 Wind Site Selections
Wind data used in the study and described previously in Section on “Wind Data
Development” consists of numerous (54,846) 2 km x 2 km grid cells at 100M tower height
spanning the Canadian continent. Each grid cell represents eight 2MW wind turbines.
Figure 4-13, depicts locations of each grid cell provided in this study. Each grid cell has
numerous data types spanning years 2008 through 2010. Of particular interest were the
production data from each grid cell that included profiles of 10 minute wind power
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Assumptions and Scenarios
production and hourly forecast data for day ahead, 6-hour ahead, 4-hour ahead, and 1-hour
ahead production.
Figure 4-13: Wind Grid Cell Locations
Given the large amount of data encompassed in the 54,846 grid cell dataset, it was
determined by the study team to group and aggregate the grid cell data to create individual
utility size wind sites for further analysis. These wind sites consist of an aggregation of grid
cells that are located within a 10 km2 area. To do this, a grid of 10 km x 10 km square areas
were tiled from east to west and south to north over the region containing the individual grid
cells. The boundary limits in the 10 km2 area are shown in Table 4-14. All grid cells within
the boundaries of each 10 km2 area were aggregated into a single wind site and assigned a
unique site ID with a location central to all grid cells making up the wind plant as shown in
Figure 4-14. Figure 4-15 shows additional detail of grid cell to wind plant aggregation. Each
unique grid cell was used only once in the wind plant development. In other words no two
wind plants share any grid cells. The aggregating process consolidated all grid cells into
4984 unique wind sites. Each wind site consists of 1 to 28 grid cells (Note that technically a
10x10 km grid should only accommodate 25 grid cells. However, during the aggregation
process the central point of a 2 km x 2 km grid cell was used for the aggregation. If only a
portion of the grid cell fell within the 10 km x10 km grid aggregation, the entire grid cell was
included, thus creating a few sites with more than 25 grid cells included). A distribution of
the number and size of different wind sites resulting from the aggregation is shown in
profiles for each wind plant were calculated that included 10-minute production and hourly
forecasts for day ahead, 6-hour ahead, 4-hour ahead and 1-hour ahead. Site ID’s were
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Final Report – Section 4
Pan-Canadian Wind Integration Study (PCWIS)
Assumptions and Scenarios
selected in the development of each scenario and used in the evaluation of reserve
requirements, statistical analysis and sub hourly analysis described in other sections in the
report.
Table 4-14: Wind Plant Aggregation Boundaries
Furthest point
Longitude
Latitude
North
-136.027
60.000
South
-82.297
42.170
East
-52.820
47.369
West
-136.203
59.979
Figure 4-14: Red Dots Represent Wind Plants and Black Dots Represent Grid Cells
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Assumptions and Scenarios
Figure 4-15: Example of 10 km x 10 km Areas That Are Tiled To Identify Grid Cells To Be Aggregated Into
Wind Plants
Figure 4-16 shows the number of wind sites at different rated capacities, which range from
16 MW to 432 MW.
600
Number of Plants
500
400
300
200
100
0
16
32
48
64
80
96
112
128
144
160
176
192
208
224
240
256
272
288
304
320
336
352
368
384
400
416
432
448
464
0
Plant Rated Capacity (MW)
Figure 4-16: Number of Wind Sites at Different Rated Capacities
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Assumptions and Scenarios
Table 4-15 shows the summary statistics for grid cell aggregation by province.
Table 4-15: Summary Statistics for Grid Cell Aggregation by Province
BC
AB
SK
MB
ON
QC
NB
NS
PEI
NL
CAN
6,505
9,570
2,379
2,341
12,179
16,441
613
1,686
473
2,659
54,846
1,033
706
167
187
997
1,382
78
171
44
219
4,984
Total Site Capacity
(MW)
104,080
153,120
38,064
37,456
194,864
263,056
9,808
26,976
7,568
42,544
877,536
Avg. Site Capacity
(MW)
101
217
228
200
195
190
126
158
172
194
176
Max Site Capacity
(MW)
416
448
432
432
432
448
416
416
416
416
448
16
16
16
16
16
16
16
16
16
16
16
Avg. Site Capacity
Factor (%)
27.5%
32.4%
37.1%
37.2%
35.4%
35.8%
37.5%
39.1%
39.3%
38.8%
33.9%
Max Site Capacity
Factor (%)
44.5%
42.6%
40.8%
40.5%
43.2%
48.0%
45.3%
48.5%
42.3%
47.1%
48.5%
Min Site Capacity
Factor (%)
8.6%
7.1%
33.3%
23.4%
24.8%
20.6%
28.0%
33.6%
36.9%
26.4%
7.1%
265,962
446,555
123,793
122,735
608,568
817,104
32,860
92,512
25,640
143,273
2,679
TWh
Number of Grid Cells
Number of Aggregated
Sites
Min Site Capacity (MW)
Total Available Energy
(GWh)
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Final Report – Section 4